Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in a survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.

翻译：基于森林的方法最近在非参数处理效果估计方面越来越受欢迎。 以森林为基础的方法在非参数处理效果估计方面得到了支持。 以这一工作为基础,我们引入了因果生存森林,这可用于在生存和观察环境中估计不同处理效果,在这种环境中,结果可能是右审查的结果。 我们的方法依靠正对估测方程,在无根据的情况下对审查效果和选择效果进行有力的调整。 在我们的实验中,我们发现我们的方法与一些基线相比运行良好。